A CNN-RBPNN Model With Feature Knowledge Embedding and its Application to Time-Varying Signal Classification

A CNN-RBPNN Model With Feature Knowledge Embedding and its Application to Time-Varying Signal Classification

A novel technique, combining the feature extraction mechanisms of a convolutional neural network (CNN) with the classification method of a radial basis probability neural network (RBPNN), is proposed for small sample set modeling and feature knowledge embedding in multi-channel time-varying signal c...

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Bibliographic Details
Main Authors: Lu Wu, Yinglong Wang, Shaohua Xu, Kun Liu, Xuegui Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Time-varying signal classification
CNN feature extraction
radial basis probability network
prior feature knowledge embedding
small sample set modelling
Online Access:https://ieeexplore.ieee.org/document/9110890/

Internet

https://ieeexplore.ieee.org/document/9110890/

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